Gene-Based Association Tests Using New Polygenic Risk Scores and Incorporating Gene Expression Data

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Gene-based association studies integrate genome-wide association studies (GWAS) with expression quantitative trait locus (eQTL) data to increase statistical power.
• Polygenic risk scores (PRS) capture the genetic liability of complex traits.

Purpose of the Study:

• To propose a novel gene-based statistical method for identifying genes associated with phenotypes.
• To leverage gene expression and PRS for enhanced association testing.

Main Methods:

• Utilized a generalized linear model to associate phenotypes with gene expression and PRS.
• Employed a score-test statistic for phenotype-gene association testing.
• Developed a new gene-based statistical method integrating gene expression and PRS.

Main Results:

• Simulation studies confirmed correct type I error rates for the new method.
• The proposed method demonstrated increased statistical power compared to methods using only gene expression or PRS.
• Real data analysis on UK Biobank asthma data confirmed the method's applicability to GWAS.

Conclusions:

• The novel gene-based method effectively identifies phenotype-associated genes by integrating gene expression and PRS.
• This approach offers improved statistical power in genetic association studies.
• The method is practical for real-world GWAS applications, as shown in the asthma study.